Methods related to real-time cancer diagnostics at endoscopy utilizing fiber-optic Raman spectroscopy

ABSTRACT

A method of achieving instrument independent measurements for quantitative analysis of fiber-optic Raman spectroscope system, the system comprising a laser source, a spectroscope and a fiber optic probe to transmit light from the laser source to a target and return scattered light to the spectroscope, the method comprising transmitting light from the laser source to a standard target having a known spectrum, recording a calibration spectrum of the scattered light from the standard target, comparing the known spectrum and the calibration system and generating a probe and/or probe-system transfer function, and storing the transfer function. Further provided is a method of performing real-time diagnostic Raman spectroscopy optionally in combination with the other disclosed methods.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to the following patent applications:(1) Patent Cooperation Treaty Application PCT/SG2013/000273 filed Jul.2, 2013; (2) U.S. Patent Application 61/667,384, filed Jul. 2, 2012; anda (3) Great Britain Patent Application 1307338.2, filed Apr. 23, 2013,each of the above cited applications is hereby incorporated by referenceherein as if fully set forth in its entirety.

FIELD

The present disclosure relates to an on-line biomedical spectroscopysoftware platform for real-time cancer diagnostics at endoscopy andmethods for instrument-independent measurements for quantitativeanalysis in fiber-optic Raman spectroscopy.

BACKGROUND

Raman spectroscopy is a technique which uses inelastic or Ramanscattering of monochromatic light. Conventionally, the monochromaticlight source is a laser in the visible or near infrared (“NIR”) range.The energy of the scattered photons is shifted up or down in response tointeraction with vibrational modes or excitations in the illuminatedmaterial, varying the wavelength of the scattered photons. Accordingly,the spectra from the scattered light can provide information about thescattering material.

NIR Raman spectroscopy is known as a potential technique forcharacterisation and diagnosis of precancerous and cancerous cells andtissue in vivo in a number of organs. The technique is desirable as itcan be non-invasive or minimally invasive, not requiring biopsies or theother removal of tissue. It is known to use NIR Raman spectroscopy intwo wavelength ranges. The first is the so-called fingerprint (“FP”)range, with wave numbers from 800 to 1800 cm⁻¹, owing to the wealth ofhighly specific bimolecular information, for example from protein, DNAand lipid contents, contained in this spectral region for tissuecharacterisation and diagnosis. The disadvantage of this wavelengthrange is, that when used with a commonly used 785 nm laser source, thestrong tissue autofluorescence background signal can be generated.Further, where the probe uses optical fiber, a Raman signal is scatteredfrom the fused silica in the optical fibers. In particular, where acharge-coupled device (“CCD”) is used to measure the scattered spectra,the autofluorescence signal can saturate the CCD and interfere with thedetection of the inherently very weak Raman signals in this wavelengthrange.

Another problem with fiber-optic Raman spectroscopy as a technique isthat of standardization of instruments. The fiber-optic Ramanspectroscopy technique has mainly been limited to single systems and noattempts have been made to transfer into multi-centre clinical trials orroutine medical diagnostics. This is mainly because Raman spectrometerinstruments are generally dissimilar (i.e., optics, response function,alignment, throughput etc.) and in general produce very different Ramanspectra. Further, fiber optic Raman probes have limited lifetimes andmust be replaced or interchanged periodically. Unfortunately, Raman dataacquired using different fiber optic probes cannot be compared, becauseeach fiber optic probe has its own unique background as well as beingassociated with different transmissive spectral properties. Thedifferent transmissive characteristics significantly distort thespectral intensities making the tissue Raman spectra obtained withdifferent fiber optic probes incomparable. As a consequence,multivariate diagnostic algorithms developed on a primary clinicalplatform cannot be applied to secondary clinical platforms. Inparticular, the quantitative measurement of tissue Raman intensity isone of the most challenging issues in fiber optic biomedical Ramanapplications. The instrument/fiber probe-independent intensitycalibration and standardization is essential to the realization ofglobal use of fiber optic Raman spectroscopy in biomedicine. For thisreason, a multivariate statistical diagnostic model constructed using a‘master’ probe cannot be applied to spectra measured with a ‘slave’probe. In order for Raman technique to become a widespread tool forcancer screening on a global scale, there is a need to standardize bothRaman spectrometers and fiber optic probes especially for biomedicalapplications. Most of the reported studies have focused on inter-Ramanspectrometer standardization for measurements of simple chemicalmixtures without fiber optic probes. In general Raman spectroscopy ofsimple chemical mixtures cannot be compared with the fiber optic Ramanspectroscopy of heterogeneous biological tissue samples.

A further problem with standardizing results across instruments is thatof spectral variation associated with the laser excitation power.Conventionally, Raman spectra are normalized which preserves the generalspectrum shape, but this removes the absolute quantitative spectralcharacteristics. It has been known to attempt to monitor delivered laserpower in fibre-optic Raman probes by, for example, embedding a diamondin the fibre tip or locating a polymer cap in the laser light path as areference. However, these solutions are not satisfactory and may causeinterferences in the required spectral regions.

A further problem in using optical spectroscopic techniques (includingreflectance fluorescence and Raman) for in vivo diagnosis of cancer andprecancerous conditions is that data analysis mostly been limited topost-processing and off-line algorithm development. This is true forendoscopic analysis because a large number of spectra collected duringendoscopy are outliers. It would be useful to have a system that allowsfor real-time diagnosis for endoscopy.

SUMMARY

According to a first aspect there is provided a method of calibrating afiber-optic Raman spectroscope system, the system comprising a lasersource, a spectroscope and a fiber optic probe to transmit light fromthe laser source to a target and return scattered light to thespectroscope, the method comprising transmitting light from the lasersource to a standard target having a known spectrum, recording acalibration spectrum of the scattered light from the standard target,comparing the known spectrum and the calibration system and generating atransfer function, and storing the transfer function.

The method may further comprise the steps of subsequently illuminating atest subject, recording a spectrum and correcting the spectrum inaccordance with the stored transfer function.

The method may comprise recording calibration spectra for each of aplurality of fiber optic probes, calculating a transfer function for thesystem including each of said probes, and associating the transferfunction with the corresponding probe.

The spectrometer has an associated spectrometer transfer function andthe probe may have an associated probe transfer function, and thetransfer function may be a function of the spectrometer transferfunction and the probe transfer function.

The method may comprise, on a primary spectrometer system, calculating afirst transfer function with a primary fiber optic probe, and a secondtransfer function with a secondary fiber optic probe, and calculating a(inter-probe) calibration function based on the first transfer functionand second transfer function.

The method may comprise associating the calibration function with thesecondary fiber optic probe.

The method may comprise, on a secondary spectrometer system, using theprimary fiber optic probe and generating a secondary system transferfunction and storing the secondary system transfer function.

The method may comprise using the secondary fiber optic probe with thesecondary spectrometer system and modifying the stored secondary systemtransfer function in accordance with the calibration function.

The method may comprise the initial step of performing a wavelength-axiscalibration of the secondary spectrometer system in accordance with theprimary spectrometer system.

According to a second aspect there is provided a method of operating aRaman spectroscope system, the system comprising a laser source, aspectroscope and a fiber optic probe to transmit light from the lasersource to a target and return scattered light to the spectroscope, themethod comprising transmitting light from the laser source to a targethaving a known spectrum, recording a spectrum of the scattered lightfrom the target, and modifying the recorded spectrum in accordance witha stored transfer function.

The stored transfer function may be associated with the spectrometer andthe fiber optic probe.

The stored transfer function may be associated with the spectrometer anda primary fiber optic probe and the method may further comprisemodifying the stored transfer function in accordance with a storedcalibration function associated with the fiber optic probe.

According to a third aspect there is provided a Raman spectroscopesystem comprising a laser source, a spectroscope and a fiber optic probeto transmit light from the laser source to a target and return scatteredlight to the spectroscope, and a stored transfer function, the systembeing operable to transmit light from the laser source to a targethaving a known spectrum, record a spectrum of the scattered light fromthe target, and modify the recorded spectrum in accordance with thestored transfer function.

The stored transfer function may be associated with the spectrometer andthe fiber optic probe.

The stored transfer function may be associated with the spectrometer anda primary fiber optic probe and the method may further comprisemodifying the stored transfer function in accordance with a storedcalibration function associated with the fiber optic probe.

According to a fourth aspect there is provided a method of estimatingthe laser power transmitted in a Raman spectrometer system, the systemcomprising a laser source, a spectroscope and a fiber optic probe totransmit light from the laser source to a target and return scatteredlight to the spectroscope, the method comprising transmitting light fromthe laser source to a plurality of targets, for each target, measuringthe transmitted power of the light from the laser source and thespectrum of the scattered light at the spectroscope, performing amultivariate analysis of the captured spectra with the measuredtransmitted power as a dependent variable, and storing a resultingmodel.

The method may comprise the step of transmitting laser light to a testtarget, supplying a captured spectrum to the model, and calculating anestimate of the transmitted power.

According to a fifth aspect there is provided a method of subtracting abackground signal from a fiber-optic Raman spectroscope system having afiber-optic probe, the method comprising the steps of;

a) storing a background spectrum,

b) receiving a test spectrum,

c) estimating a background contribution using one or more referencepeaks,

d) multiplying the background spectrum by a correction factor based onthe estimated background contribution and subtracting it from the testspectrum,

e) checking the test spectrum for a remaining background contribution,and

f) if the background contribution is negligible, outputting the testspectrum, otherwise repeating steps (c) to (e).

The one or more reference peaks may comprise one or more peakscorresponding to silica or sapphire in the fiber-optic probe.

According to a sixth aspect there is provided a computer implementedmethod for real-time diagnosis using Raman spectroscopy duringendoscopy. The method comprises receiving at least one spectrumassociated with a tissue; analyzing the at least one spectrum in a modelthat uses the spectrum to determine a score wherein said score indicatesa likelihood of the tissue being cancerous; and outputting said score.

In some embodiments the model is generated using an interpretationfunction selected from the group consisting of partial leastsquares-discriminant analysis, principal component analysis lineardiscriminant analysis, ant colony optimization linear discriminantanalysis, classification and regression trees, support vector machine,and adaptive boosting.

In some embodiments the at least one spectrum is generated by Ramanspectroscopy. Analyzing the at least one spectrum in a model maycomprise analyzing the at least one spectrum in a first model and asecond model. In some embodiments the model is selected based on thetissue analyzed. In some embodiments the score indicates whether thetissue is normal, intestinal metaplasia, dysplasia or neoplasia.

In some aspects analyzing the at least one spectrum comprises:performing outlier analysis; and responsive to the outlier analysisdetermining that the at least one spectrum is an outlier, rejecting thespectrum. Performing outlier analysis may comprise principal componentanalysis.

In some aspects an audio emitting device emits an audio signalresponsive to the outlier analysis determining that the at least onespectrum is an outlier. Responsive to the determination that the spectrais an outlier method instructs the spectrometer to acquire an additionalat least one spectrum which is received by the system for analysis.

In some embodiments, the audio emitting device to emit an audio signalidentifying the tissue as normal, dysplasia or neoplasia. In someembodiments, the audio signal associated with each diagnosis isdifferent and also different from an audio signal associated with thedetermination of an outlier spectrum.

In some embodiments, the diagnosis takes place during the endoscopicprocedure.

Also provided are systems for carrying out the computer-implementedmethods as well as non-transitory computer readable media withinstructions thereon for carrying out the computer-implemented methods.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosed system and methods are described by way ofexample only with reference to the accompanying drawings.

FIG. 1 is a diagrammatic illustration of a Raman spectroscopic systemaccording to one embodiment.

FIG. 1a is a view of the end of the endoscope of FIG. 1 on a largerscale.

FIG. 1b is a view of the Raman probe of the endoscope of FIG. 1a in moredetail.

FIG. 2 is a graph illustrating a comparison of measured fluorescencespectra to a reference standard. The calibration functions are alsoshown.

FIG. 3 is a diagrammatic illustration of a first calibration method.

FIG. 4a is a flow chart showing a first process for use with the firstcalibration method.

FIG. 4b is a flow chart showing a first part of a second process for usewith the first calibration method.

FIG. 4c is a flow chart showing a second part of a second process foruse with the first calibration method.

FIG. 5 is a graph showing the wavelength alignment of an argon/mercurylamp among a primary spectrometer and a secondary spectrometer.

FIG. 6 is a graph showing the spectral calibration of a primaryspectrometer and a secondary spectrometer using second calibrationmethod.

FIG. 7a is a flow chart showing a first process for use with the firstcalibration method.

FIG. 7b is a flow chart showing a first part of a second process for usewith the second calibration method.

FIG. 7c is a flow chart showing a second part of a second process foruse with the second calibration method.

FIG. 8 is a graph showing fluorescent standards measured with master andslave probes and a probe calibration transfer function,

FIG. 9a is a graph of tissue Raman spectra comparing uncalibratedprimary and secondary spectrometers with master and slave proberespectively.

FIG. 9b is a graph of tissue Raman spectra from primary and secondaryspectrometers with master and slave probe respectively afterrecalibration using a first calibration method.

FIG. 9c is a graph showing spectra from primary and secondaryspectrometers with master and slave probe respectively afterrecalibration using a second calibration method.

FIG. 10 Principal component analysis score scatter plot on in vivotissue Raman spectra from the gastric before and after calibration:

FIG. 11 is a graph showing background spectral peaks due to the fiberprobe in a Raman spectrum.

FIG. 12 is a graph showing variation of the Raman spectra withexcitation laser power.

FIG. 13a is a flow chart illustrating a method of generating a model forestimating laser power.

FIG. 13b is a flow chart illustrating a method of estimating laserpower,

FIG. 14a is a graph showing the root mean square error for any number ofincluded latent variables,

FIG. 14b shows the loading and regression factor for the latentvariables of the method of FIG. 10,

FIG. 15 is a graph showing measured laser power against predicted laserpower in in vivo test subjects.

FIG. 16 is a flow chart showing a method of subtracting a probebackground signal.

FIG. 17 is a graph showing a spectrum received from a palm and thefibre-optical silica and sapphire background.

FIG. 18 is a graph comparing the Raman spectrum of FIG. 16 and thespectrum after background removal.

FIG. 19 is a flow chart showing a combination of the methods.

FIG. 20 is an architecture diagram for the system for spectralacquisition and processing flow for real-time cancer diagnosticsaccording to one embodiment.

FIG. 21 is a flow chart illustrating a schematic of the spectralacquisition and processing flow for real-time cancer diagnosticsaccording to one embodiment.

FIGS. 22A and B are a graphical user interfaces (GUI) for using thesystem for real-time cancer diagnosis according to two embodiment.

FIG. 23 is in vivo mean Raman spectra of normal (n=2465) and cancer(n=283) gastric tissue acquired from 305 gastric patients.

FIG. 24 illustrates principal component (PC) loadings calculated from aspectral training database.

FIG. 25 are scatter plots of two diagnostically significant PC scores(PC1 vs PC2).

FIG. 26 demonstrates Hotelling's T² versus Q-residuals for 105 Ramanspectra (45 normal, 30 cancer, 30 outlier) acquired from 10 prospectivegastric samples.

FIG. 27 is a scatter plot of the posterior probability values belongingto prospective normal (n=45) and cancer (n=30) gastric tissue based onPLS-DA modeling together with leave-one spectrum-out, cross-validation.

FIG. 28 illustrates receiver-operating characteristic (ROC) curvescomputed from the spectral database for retrospective prediction as wellas ROC curve for prospective prediction of normal and cancer gastrictissue.

FIG. 29 illustrates the autofluorescence-subtracted and intensitycalibrated mean in vivo tissue Raman spectra±1 SD of inner lip by usingdifferent 785-nm laser excitation powers (i.e., 10, 30 and 60 mW).

FIG. 30a illustrates the relationship between the actual and thepredicted laser excitation powers using PLS regression model based onthe leave-one subject-out, cross-validation as well as the linear fit tothe data.

FIG. 30b illustrates the relationship between the actual and thepredicted laser excitation power using PLS regression based on theindependent validation.

FIG. 31 illustrates Raman spectra of gelatin tissue phantoms preparedwith different concentrations (i.e., 20, 25, 30, 35, 40, 45, and 50% byweight) measured at 60 mW laser excitation power.

FIG. 32 illustrates the correlationship between the actual and predictedgelatin concentrations in tissue phantoms after the correction with thepredicted laser power.

FIG. 33 illustrates representative in vivo raw Raman spectrum acquiredfrom the Fossa of Rosenmüller with 0.1 s during clinical endoscopicexamination. Inset of FIG. 33 is the processed tissue Raman spectrumafter removing the intense autofluorescence background.

FIG. 34 illustrates in vivo (inter-subject) mean Raman spectra±1standard deviations (SD) of posterior nasopharynx (PN) (n=521), fossa ofRosenmüller (FOR) (n=157) and laryngeal vocal chords (LVC) (n=196). Notethat the mean Raman spectra are vertically displaced for bettervisualization. In vivo fiber-optic Raman endoscopic acquisitions fromposterior nasopharynx (upper) fossa of Rosenmüller (mid) and laryngealvocal chords (lower) under white light reflectance (WLR) and narrowband(NB) imaging guidance are also shown.

FIG. 35 illustrates in vivo (intra-subject) mean Raman spectra±1 SD ofPN (n=18), FOR (n=18) and LVC (n=17). Note that the mean Raman spectraare vertically displaced for better visualization.

FIG. 36 illustrates the comparison of difference spectra±1 SD ofdifferent anatomical tissue types (inter-subject): [posteriornasopharynx (PN)−laryngeal vocal chords (LVC)]; [posterior nasopharynx(PN)−fossa of Rosenmüller (FOR)] and [laryngeal vocal chords (LVC)−fossaof Rosenmüller (FOR)].

FIG. 37 illustrates in vitro Raman spectra of possible confoundingfactors from human body fluids (nasal mucus, saliva and blood).

FIG. 38 illustrates PC loadings resolving the biomolecular variationsamong different tissues in the head and neck, representing a total of57.41% (PC1: 22.86%; PC2: 16.16%; PC3: 8.13%; PC4 6.22% PC5: 4.05%) ofthe spectral variance.

FIG. 39 provides box charts of the 5 PCA scores for the different tissuetypes (i.e., PN, FOR and LVC). The line within each notch box representsthe median, but the lower and upper boundaries of the box indicate first(25.0% percentile) and third (75.0% percentile) quartiles, respectively.Error bars (whiskers) represent the 1.5-fold interquartile range. Thep-values are also given among different tissue types.

FIG. 40A illustrates mean in vivo confocal Raman spectra of squamouslined epithelium (n=165), columnar lined epithelium (n=907), Barrett'sesophagus (n=318), high-grade dysplasia (n=77) acquired during clinicalendoscopic examination.

FIGS. 40B-E illustrate B) Representative histological sectioned-slides(hematoxylin and eosin (H&E) stained) corresponding to the measuredtissue sites. Squamous lined epithelium (C) Columnar lined esophaguswith absence of goblet cells, ×200; (D) Barrett's esophagus where thenormal stratified squamous epithelium is replaced by intestinalmetaplastic epithelium containing goblet cells, ×200; (E) High-gradedysplasia showing both architectural and cytological atypia as well ascrowded crypts with branching and papillary formation, cytologicalpleomophism and loss of polarity; ×100.

FIG. 41A illustrates two-dimensional ternary plot of the prospectiveposterior probabilities belonging to ‘normal’ columnar lined epithelium(CLE), (ii) ‘low-risk’ intestinal metaplasia (IM) (iii) ‘high-risk’high-grade dysplasia (HGD) using confocal Raman endoscope technique.

FIG. 41B Receiver operating characteristics (ROC) curves of dichotomousdiscriminations of ‘normal’ CLE, (ii) IM (iii) ‘high-risk’ HGD. Theareas under the ROC curves (AUC) are 0.88, 0.84 and 0.90, respectively.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Provided herein is an on-line system and method for biomedicalspectroscopy (i.e., reflectance, fluorescence and Raman spectroscopy)for realizing real-time detection of neoplastic lesions in differentorgans (e.g., gastrointestinal tracts (stomach, esophagus, colon),bladder, lung, oral cavity, nasopharynx, larynx, cervix, liver, skin,etc.) at endoscopy. The diagnostic method integrates excitation sourcesynchronization, integration-time adjustment, data acquisition,preprocessing, outlier analysis and probabilistic multivariatediagnostics (i.e., partial least squares-discriminant analysis (PLS-DA),principal component analysis (PCA)-linear discriminant analysis (LDA),ant colony optimization (ACO)-LDA, classification and regression trees(CART), support vector machine (SVM), adaptive boosting (AdaBoost) etc.)including multi-class diagnostics based on comprehensive spectraldatabases (i.e., Raman, fluorescence, reflectance) of different organs.

In one embodiment, the disclosed system and method integrates theon-line diagnostic framework with the recently developed multimodalimage-guided (WLR/NBI/AFI) Raman spectroscopic platform for earlydiagnosis and detection of precancer and cancer in the upper GI atendoscopy. The accumulation of tissue Raman spectra and automaticscaling of integration time with a predefined upper limit of 0.5 sallows instant acquisition of in vivo tissue spectra with improved SNRwhile preventing CCD signal saturation. This is especially important forendoscopic diagnostics where the autofluorescence intensity variessignificantly among different anatomical regions (e.g., antrum and bodyin the gastric, bronchi in the lung) likely caused by distinctendogenous fluorophores in the tissue.

With specific reference now to the drawings in detail, it is stressedthat the particulars shown are by way of example and for purposes ofillustrative discussion of the preferred embodiments, and are presentedin the cause of providing what is believed to be the most useful andreadily understood description of the principles and conceptual aspectsof the disclosed system and methods. In this regard, no attempt is madeto show structural details of the disclosed system and methods in moredetail than is necessary for a fundamental understanding of thedisclosed system and methods, the description taken with the drawingsmaking apparent to those skilled in the art how the several forms of thedisclosed system and methods may be embodied in practice.

Before explaining at least one embodiment of the disclosed system andmethods in detail, it is to be understood that the disclosure is notlimited in its application to the details of construction and thearrangement of the components set forth in the following description orillustrated in the drawings. The disclosed system and methods areapplicable to other embodiments or of being practiced or carried out invarious ways. Also, it is to be understood that the phraseology andterminology employed herein is for the purpose of description and shouldnot be regarded as limiting.

Referring now to FIG. 1, a diagnostic instrument comprising an endoscopesystem according to one embodiment is shown at 10. The endoscope itselfis shown at 11 and an instrument head of the endoscope 11 is generallyillustrated in FIG. 1a . To provide for guidance and visual viewing ofthe area being tested, the endoscope 11 is provided with a suitablevideo system. Light from a xenon light source is transmitted toillumination windows 15 in the end of the endoscope 12. CCDs 16 and 17,responsive to white light reflection imaging, narrowband imaging orautofluorescence imaging, receive the reflected light and transmit videodata to allow for visual inspection of the tested tissues and forguidance of the endoscope to a desired position. The confocal Ramanprobe head is showing at 18, and in more detail in FIG. 1 b.

The Raman spectroscopy system is generally shown at 20. A monochromaticlaser source is shown at 21, in the present example a diode laser withan output wavelength of about 785 nm. Light from the laser diode 21 ispassed through a proximal band pass filter 22, comprising a narrowbandpass filter being centred at 785 nm with a full width half max of ±2.5nm. The light is passed through a coupling 23 into an excitation opticalfiber 25 provided as part of a fiber bundle. The excitation fiber 25 hasa diameter of 200 μm and a numerical aperture (‘NA’) of 0.22. Lighttransmitted by the excitation fiber 25 enters a ball lens 26 at the endof the endoscope 11, in the present example comprising a sapphire balllens with a diameter of about 1.0 mm and a refractive index n=1.77. Asillustrated in FIG. 1b , transmitted light from the excitation opticalfiber 25 is internally reflected within the ball lens 26. Where the balllens is in contact with the tissue to be tested, as shown here at 27,the transmitted light from the excitation fiber 25 at least in partundergoes Raman scattering within the tissue 27, to a depth of ˜140 μm.The scattered light is again internally reflected in the ball lens 26and received in a plurality of collection fibers 28, also provided aspart of the fiber bundle. In the present example twenty-six 100 μmcollection fibers are used, with an NA of 0.22. The collection fibres 28may be arranged in any suitable configuration, for example in a circulararrangement surrounding the excitation fiber 25.

Collected scattered light returned by collection fibers 28 is passedthrough a long pass inline collection filter 29 which similarly has acutoff at ˜800 nm. The configuration of sapphire ball lens 26,excitation and collection fibers 25, 28, band-pass filters 22, andlong-pass filter 29 provides a good system for selectively collectingbackscattered Raman photons from the tissue 27.

The scattered returned light is then separated at spectrograph 30 andthe resulting spectrum is imaged at a light-sensing array 34, in thepresent example a charge-couple device (‘CCD’). A computer shown at 35controls the operation of the system, processes and stores the spectraand control data, and provides results and data to a user.

In one embodiment, the computer 35 comprises at least one processorcoupled to a chipset. Also coupled to the chipset are a memory, astorage device, a keyboard, a graphics adapter, a pointing device, anaudio emitting device and a network adapter. A display is coupled to thegraphics adapter. In one embodiment, the functionality of the chipset isprovided by a memory controller hub and an I/O controller hub. Inanother embodiment, the memory is coupled directly to the processorinstead of the chipset.

The storage device is any device capable of holding data, like a harddrive, compact disk read-only memory (CD-ROM), DVD, or a solid-statememory device. The memory holds instructions and data used by theprocessor. The pointing device may be a mouse, track ball, or other typeof pointing device, and is used in combination with the keyboard toinput data into the computer system. The graphics adapter displaysimages and other information on the display. The network adapter couplesthe computer system to a local or wide area network.

As is known in the art, a computer 35 can have different and/or othercomponents than those described previously. In addition, the computercan lack certain components. Moreover, the storage device can be localand/or remote from the computer (such as embodied within a storage areanetwork (SAN)).

As is known in the art, the computer is adapted to execute computerprogram modules for providing functionality described herein. As usedherein, the term “module” refers to computer program logic utilized toprovide the specified functionality. Thus, a module can be implementedin hardware, firmware, and/or software. In one embodiment, programmodules are stored on the storage device, loaded into the memory, andexecuted by the processor.

Embodiments of the entities described herein can include other and/ordifferent modules than the ones described here. In addition, thefunctionality attributed to the modules can be performed by other ordifferent modules in other embodiments. Moreover, this descriptionoccasionally omits the term “module” for purposes of clarity andconvenience.

The computer 35 also performs preprocessing the spectral data. As themeasured tissue Raman spectra are substantially obscured by the tissueautofluorescence background, preprocessing of in vivo tissue Ramanspectra is necessary to extract the weak Raman signals. The raw Ramanspectra measured from in vivo tissue represent a combination of the weakRaman signal, intense autofluorescence background, and noise. Thespectra are first normalized to the integration time and laser power.The spectra are then smoothed using a first-order Savitzky-Golaysmoothing filter (window width of 3 pixels) to reduce the noise. Afifth-order polynomial was found to be optimal for fitting theautofluorescence background in the noise-smoothed spectrum, and thispolynomial is then subtracted from the raw spectrum to yield the tissueRaman spectrum alone. The computer 35 can also including diagnosticalgorithms for precancer and cancer detection.

Spectrometer and Fibre-Optic Probe Calibration

It is known that different spectrometers will have different transferfunctions, i.e. will show differing intensity variations within spectraeven when illuminated using the same source. As illustrated in FIG. 2,the spectrum from a standard source is shown. The standard source inthis example is a fluorescent standard target that emits a knownfluorescent spectrum when excited by a laser such as laser source 21.The fluorescent standard target must be consistent and stable and emit abroad fluorescence spectrum under a laser excitation (e.g., 785 nm). Thefluorescence spectrum must be stable over time and efficientlycharacterize the spectral transmissive properties over the entirespectral region of interest (e.g., 400-1800 cm⁻¹, 2000-3800 cm⁻¹). Anexample is chromium-doped glass. The resulting spectra from twospectrometers are shown, which are clearly different. To compensate forthe spectrometer response, or transfer function, it is known to apply acalibration function which will correct the spectrum received from thespectrometer. Examples and calibration functions are shown in FIG. 2which, when applied to the corresponding spectrum of the spectrometer,will bring the spectrum into line with the known standard spectrum.

Using a fluorescent standard source, the transfer function, i.e. thewavelength-dependent response of the spectrometer, can be given by

${F(\lambda)} = \frac{S(\lambda)}{T(\lambda)}$(eqn. 1) where F(λ) is the correct fluorescent standard spectrum, S(λ)is the measured spectrum of the fluorescent standard source and T(λ) isthe transfer function of the spectrometer. Accordingly, as T(λ) isknown, a correctly calibrated Raman spectrum of a new sample R(λ) can becalculated by

${R(\lambda)} = \frac{S(\lambda)}{T(\lambda)}$(eqn. 2) where S(λ) is the measured sample spectrum.

The transfer function T(λ) is a function both of the spectrometertransfer function T_(S)(λ) and a probe transfer function T_(P)(λ).Equation 2 can therefore be written as

${R(\lambda)} = \frac{S(\lambda)}{{T_{S}(\lambda)}{T_{P}(\lambda)}}$(eqn. 3). As fibre-optic probes are replaceable and may be consumables,it will be apparent that when a new probe with a new probe transferfunction T_(P) is inserted, the overall transfer function of the systemwill change.

Referring now to FIG. 3, a primary or master spectrometer is shown at 50and a secondary or slave spectrometer is shown at 51. The spectrometers50, 51 each have a configuration similar to that shown in FIG. 1, butmay have different fibre probes and spectrograph characteristics.Ideally, the personal computer 35 controlling each spectrograph uses acommon library of programs to provide control of the system and dataprocessing, and it is therefore desirable that characteristics of theprimary and secondary spectrometers 50, 51 are consistent. In thisexample, the primary spectrometer 50 is associated with the primary ormaster probe 52, and the secondary spectrometer 51 is associated with aplurality of secondary or slave probes shown at 53 a, 53 a, 53 b. Ineach case, the calibration is performed with reference to a standardfluorescent source diagrammatically illustrated at 54.

A first method of calibration is shown in FIG. 4a . At step 60, thesecondary spectrometer is wavelength calibrated in accordance with theprimary spectrometer. In this case, wavelength-axis calibration of thesecondary spectrometer 51 is performed, for example using anargon-mercury spectral lamp or a chemical sample with defined spectrallines, and pixel resolution matching using linear interpolation is thenperformed to ensure that the size of the axis of the second spectrometermatches that of the primary spectrometer. The results of thiscalibration are shown in FIG. 5, where the spectra from the primary andsecondary spectrometers 50, 51 show the spectral lines from the lampprecisely aligned. At step 61, calibration is performed for the secondspectrometer and the probe 53 a using a fluorescent source 54. In asimilar manner to the graph of FIG. 2, a spectrum will be recorded fromthe fluorescent source, and a transfer function can then be calculatedto bring the measured spectrum into line with the known spectrum, andstored, for example by the personal computer 35. At step 62, thespectrometer 51 may then be used for in vivo Raman testing or otherwise,and the measured Raman spectra can be corrected using the calibrationfunction recorded at step 61.

When probe 53 a is discarded and it is desired to carry out tests on anew subject, a replacement probe 53 b may be substituted, in which casethe method of FIG. 4a is repeated.

In an alternative process as illustrated in FIGS. 4b and 4c , aplurality of calibration functions may first be recorded for thesecondary spectrometer and a plurality of secondary probes. At step 60in FIG. 4b , as in FIG. 4a , the secondary spectrometer 51 is calibratedfor consistency with primary spectrometer 60. At step 61, a calibrationfunction for secondary probe 53 a is measured, and at step 63 thiscalibration function is stored and associated with probe 53 a in someway, for example by saving the calibration function as a computer file56 a tagged with a reference number corresponding to the secondary probe53 a. As shown by arrow 64, this process is then repeated for any numberof probes 53 b, . . . , 53 n to provide a stock or reserve of probes. Asshown in FIG. 4c , when it is desired to carry out testing using thespectrometer 51, at step 60 the spectrometer is calibrated in accordancewith the primary spectrometer 50 as above. At step 65 probe 53 n isinstalled on the system and a corresponding stored transfer function 56n retrieved. At step 66, tests using the secondary spectrometer 51 maybe performed and calibrated using the retrieved calibration function 56n.

An alternative approach is illustrated with reference to FIG. 6, inwhich the slave or secondary probes 53 a, . . . , 53 n are calibrated onthe primary or master 50. In accordance with equation 2, where theprimary spectrometer is tested with a primary or master probe withtransfer function T_(PP)(λ) and a secondary or slave probe with transferfunction T_(SP)(λ), the spectrum from the fluorescent source F(λ) willresult in a spectrum S_(pp)(λ) for the primary probe, where

${F(\lambda)} = \frac{S_{PP}(\lambda)}{{T_{S}(\lambda)}{T_{PP}(\lambda)}}$(eqn. 4) and a spectrum S_(SP)(λ) using the secondary probe, where

${F(\lambda)} = \frac{S_{SP}(\lambda)}{{T_{S}(\lambda)}{T_{SP}(\lambda)}}$(eqn. 5). Equations 4 and 5 can be divided to relate the two probetransfer values through a probe calibration function T_(CF), where

$T_{CF} = {\frac{T_{SP}(\lambda)}{T_{PP}(\lambda)} = \frac{S_{SP}(\lambda)}{S_{PP}(\lambda)}}$(eqn. 6). Consequently, from equations 2 and 6, when the secondaryspectrometer is used with the secondary probe, the measured spectrumS(λ) and Raman spectrum R(λ) are related by

${R(\lambda)} = {\frac{S(\lambda)}{{T_{S}(\lambda)}{T_{SP}(\lambda)}} = \frac{S(\lambda)}{{T(\lambda)}T_{CF}}}$(eqn. 7) where T(λ)=T_(S) (λ)T_(PP)(λ) is the stored system transferfunction measured for the secondary spectrometer using the master probe.

As illustrated in FIGS. 6 to 7 c, this allows any number of secondary orslave probes 53 a, 53 b, 53 n to be matched to any number of secondaryspectrometers 51 a, 51 b, 51 n. As shown in FIG. 7a , at a first step 70the secondary spectrometer 51 a is calibrated in accordance with primaryspectrometer 50 in similar manner to step 60, using the master probe 52.The system transfer function 71 a is found at step 72 by testing thesecondary spectrometer against a fluorescent standard source 54 in likemanner to the method of FIGS. 3 to 4 c. The system transfer function 71a is associated with the corresponding spectrometer 51 a in anyappropriate manner, for example in the control software or otherwise atstep 73. As shown by arrow 74, this may be repeated for any number ofsecondary spectrometer systems 51 b, . . . 51 n, to generate appropriatesystem transfer functions 71 b, . . . 71 n.

As shown in FIG. 7b , the secondary or slave probes 53 a, 53 b, . . . ,53 n are calibrated against the master probe 52. At step 75, the primaryspectrometer system 50 is suitably calibrated with the master probeagainst a fluorescent source 54, although this may be omitted if thisstep has already been performed and the transfer function associatedwith the master probe already stored. At step 76, the master probe isreplaced by probe 53 a, and the combination of the primary spectrometersystem and corresponding slave probe then tested against a fluorescentstandard 54. At step 77, a calibration function T_(CF) is calculatedfrom the ratio of the primary and secondary probe spectra and at 78 thisis recorded and stored associated with the secondary probe as shown at79 a. As shown by arrow 80, this can be repeated for any number ofsecondary probes 53 b, . . . 53 n and the corresponding calibrationfunction T_(CF) stored as shown at 79 b, . . . 79 b.

As illustrated in FIG. 7c , one of the secondary spectrometer systems 51n may be used with any one of the secondary probes 53 n, as the systemtransfer function 71 a using the master probe 52 is known and thecalibration function T_(CF) relating the secondary probe 53 n to themaster probe 52 is known. As shown at step 81, the secondaryspectrometer system 51 n is calibrated in accordance with the primaryspectrometer system 50. At step 82, the secondary probe calibrationfunction T_(CF) is retrieved and the store system transfer function 71 nmodified in accordance with the stored calibration function T_(CF). Atstep 83, in vivo Raman tests or otherwise can then be performed and thecaptured Raman spectra corrected.

In any of the methods therefore, by matching the secondary spectrometercharacteristics back to the primary spectrometer characteristics, andstoring either the transfer function for the spectrometer and probecombination or a transfer function for the system incorporating a masterprobe and a calibration function for use with a secondary probe, spectracaptured using different spectrometer and probe combinations willnevertheless be consistent and comparable.

This is apparent from FIG. 8 and FIGS. 9a to 9c . FIG. 8 shows differentresponses between the master probe 52 and a secondary or slave probe 53n. The intensity response varies over the spectrum, and the calibrationfunction as shown would map the spectrum of the secondary probe to thatof the main or master probe. Uncalibrated tissue spectra from a primaryspectrometer 50 and a secondary spectrometer 51 are shown in FIG. 9a andthe differences between them are apparent. FIGS. 9b and 9c show theresults of calibration using each of the methods shown above and thespectra from the primary and secondary spectrometer are substantially inagreement.

Raman spectra were measured from the gastric with two different probes(n=902 spectra). A principal component analysis (PCA) was conductedbefore and after calibration of the secondary probe. FIG. 10 shows thePCA analysis before and after calibration of the fiber-optic probe aswell as the 95% confidence interval on different scores. It is evidentthat after calibration of the fiber-probe, the spectra falls within thesame confidence interval indicating a successful transfer among themaster and slave fiber-optic Raman probes.

Monitoring Laser Power

FIG. 11 is a graph showing a background spectrum from a fibre probe,i.e. in the absence of a tissue signal. Peaks corresponding to Ramanscattering or fluorescence within the silica of the fibre and peakscorresponding to the sapphire of the distal ball lens are apparent. FIG.12 shows a graph of Raman spectra received from in vivo tissue withdifferent levels of transmitted power. The peaks from FIG. 11 areapparent in the different lines of FIG. 12, but it will be apparent thatthe relative heights of the peaks and the continuum background vary withthe transmitted power.

Advantageously, it has been found that the spectral characteristics ofthe fibre probe and sapphire ball lens in captured Raman spectra can beused as internal reference to derive the transmitted laser power withoutrequiring the provision of any additional components in the opticaltrain. As shown in the method of FIG. 13A, at step 90, a suitably largenumber of spectra, in the present example 352, are collected and thetransmitted laser power measured. At step 91, a suitable multi-variatestatistical analysis is performed, in the present example partial leastsquares (“PLS”) regression. PLS regression reduces the dimension ofspectral data to a number of latent variables (“LV”). In this case, thevariance between the spectral variation and the dependent variable, thelaser power, is maximised so that the latent variables give a higherweight to spectral peaks that correlate well with the laser power. Byselecting an appropriate number of latent variables, a model of thelaser power as a function of the spectral characteristics can bederived, and is stored as shown at step 92. Accordingly, in operation asshown in FIG. 13b at step 93 a test spectrum is captured, for examplefrom an in vivo subject or otherwise, and at step 94 the spectral valuesare provided to the stored model. At step 95, the laser power is derivedand displayed to the operator, for example on the personal computer 35.

In the present example, a graph of the number of latent variablesincluded against the root mean square error is shown in FIG. 14a , andfour variables are selected as giving the best balance between error andcomplexity. The relative loading of the four latent variables and theregression vector are shown in FIG. 14b . In FIG. 15 example data fromreal-time measurements of 166 spectra in five subjects are shown, withthe measured laser power plotted against the power estimated by themodel. It will be apparent that the substantially linear fit shows thatthe estimated power is a good indicator of the power actually delivered.

Iterative Background Subtraction

A method of subtracting the background Raman spectrum resulting fromfluorescence, Raman scattering in the silica of the probe and thesapphire of the lens is shown with reference to FIGS. 16 to 18 Thisbackground signal is unique to each specific fiber probe. It isdesirable to remove the background from the tissue Raman spectra withoutover- or under-subtracting the background.

As shown at step 110 in FIG. 16, the background spectrum is captured andstored, for example by transmitting light from the laser source throughthe probe in the absence of a target. At step 111, the Raman spectrumfrom a test subject is received, for example from tissue. At step 112,the amount of fiber background signal in the test subject Raman spectrumis estimated using the intensity of one or more distinct referencepeaks. In the present example, the peaks may be due to silica and/orsapphire (e.g., 417 or 490 cm⁻¹). Using the estimated amount ofbackground signal, the stored background signal may be multiplied by asuitable, possibly wavelength-dependent, correction factor andsubtracted from the test spectrum (step 113).

At step 114, the spectrum is checked for the presence of remainingbackground. If the background has been fully removed, (i.e., when thesilica and sapphire signal contributes negligible to the tissue Ramanspectrum), the spectrum is passed for output or further analysis asshown at step 115. If a background signal is still present, then steps112 to 114 are repeated as shown by arrow 116.

The method need not be limited to single silica/sapphire peaks.Multivariate analysis (e.g., partial least squares and curve resolutionmethods etc.) can also be used for this purpose.

By way of example, FIG. 17 is a graph showing a Raman spectrum receivedfrom palm tissue and a background spectrum from the probe. The peaksfrom fluorescence, Raman scattering in the silica of the probe and thesapphire of the lens are apparent, superposed on the Raman spectrum fromthe palm. This background signal is unique to each specific fiber probe

As shown in FIG. 18, after the iterative process of FIG. 16 has beenperformed, the smooth Raman spectra are shown without the distinctivepeaks of the background signal but retaining the essential Ramanspectroscopic information required.

Combined System

The various disclosed methods can be used together. One embodiment ofsuch a combination is illustrated in FIG. 19. At step 100 thecalibration method can be performed such that the system transferfunction is known in accordance with a master or primary system 50 andsubsequent spectra can be appropriately corrected. At step 101,pre-processing of the signal can be performed, including smoothing andtissue background subtraction. At step 103 power monitoring can beperformed on the spectrum as discussed above and, in parallel, at step102 probe background subtraction can be performed. As shown at step 104,the information of steps 102 and 103 is provided to a suitable programon the personal computer 35 to perform other diagnostic or output steps.

In combining the disclosed calibration method with a diagnostic method,instrument-independent fiber optic Raman spectroscopy is possible forquantitative tissue analysis and characterization. This allows forcomparison of spectra taken by different instruments and also spectrataken with the same instrument but different probes. This is importantfor diagnosis in that it allows use of spectra taken on differentmachines or with different probes to be used for comparison. This isimportant for increasing accuracy of diagnosis.

Real-Time Cancer Diagnostics

An on-line biomedical spectroscopy (i.e., reflectance, fluorescence andRaman spectroscopy) system and method realizes real-time cancerdiagnostics at clinical endoscopy and can interface with cliniciansusing auditory feedback as well as graphical display of the outcome ofprobabilistic diagnostic algorithms with the predicted pathology. TakingRaman endoscopy in the gastric as an example (FIG. 22A); the method isable to predict several pathologies: normal, intestinal metaplasia,dysplasia and neoplasia. This on-line diagnostic method providesinformation to the clinician in real-time of tissue pathology that canbe used for decision-making such as biopsy guidance or tumoreradication. The system, including a GUI, is optimized for rapid dataprocessing allowing real-time diagnostics (<0.1 s) for example forclinical endoscopy

In order to address inter-anatomical and inter-organ spectral variancesthe online framework implements organ specific diagnostic models andswitches among the spectral databases of different organs (e.g.,esophagus, gastric, colon, cervix, bladder, lung, nasopharynx, larynx,and the oral cavity (hard palate, soft palate, buccal, inner lip,ventral and the tongue)). Thus, the disclosed Raman platform is auniversal diagnostic tool for cancer diagnostics at endoscopy.

FIG. 20 is an architecture diagram for a diagnostic system 115 forspectral acquisition and processing flow for real-time cancerdiagnostics according to one embodiment. The diagnostic system 115 maybe implemented on the personal computer 35. The diagnostic system 115comprises a spectral acquisition module 120, a spectral preprocessingmodule 125, an outlier analysis module 130, a multivariate analysismodule 135, a pathology module 140 and a database 142. For simplicityonly one spectral acquisition module 120, spectral preprocessing module125, outlier analysis module 130, multivariate analysis module 135,pathology module 140 and database 142 are shown but in practice many ofeach may be in operation.

Referring to FIGS. 20-22, in step 145, the spectral acquisition module120 electronically synchronizes the laser excitation source with the CCDand stores the binned read-out from the CCD in the database 142 forfurther processing. The spectral acquisition module furtherautomatically adjusts the exposure time and accumulation of spectra byscaling to within ˜85% of the total photon counts based on precedingtissue measurements, whereas an upper limit of 0.5 sec is set to realizeclinically acceptable conditions. The accumulation of multiple spectraand automatic adjustment of exposure time provides a rapid andstraightforward methodology to prevent signal saturation and to obtainhigh signal to noise ratio for endoscopic applications. If the spectralsignal saturates, the method initiates a new data acquisition withreduced integration time to prevent saturation. After the spectralacquisition, the method identifies and eliminates cosmic rays (e.g.,using the first derivative of the spectra with a 95% confidence interval(CI) over the whole spectral range set as a maximum threshold). Theidentified cosmic rays are removed by linear interpolation. The shortspectral acquisition time frame is especially useful for endoscopicapplications. The GUI illustrated at FIG. 22A illustrates the spectrumacquisition at 180.

For other applications, the spectral acquisition framework could also beused for external or internal surgical interventions or to assess tissuetypes during surgery. The real-time capability allows on-the-spotdiagnosis and could therefore be used to guide excisional margins fortumor resection. It is critical that the diagnostic information can begiven online (i.e., <0.5 sec) to aid in medical decision-making. Forskin measurements, there are less stringent demands to the measurementtime because skin spectra are acquired under more controllableexperimental settings with possibility of longer exposure times. Theonline software architecture can also apply to other areas that requirefast spectral measurements including fluorescence, reflectancespectroscopy or in different fields such as process analyticaltechnology, food sciences, forensics, etc., whereby uninterruptedreal-time screening is needed. In step 150, the spectral acquisitionmodule 120 determines if the signal is saturated. If so, it initiates anew data acquisition with reduced integration time to preventsaturation. In step 155, the spectra acquisition module 120 identifiesand eliminates cosmic rays (i.e., using the first derivative of thespectra with a 95% confidence interval (CI) over the whole spectralrange set as a maximum threshold). In one embodiment identified cosmicrays are removed by linear interpolation. Cosmic rays can be removed byother methods, including multivariate analysis, smoothing, meanfiltering, median filtering, Fourier transform, wavelets, etc.

In step 160, the spectral preprocessing module 125 scales the acquiredspectra with integration time and laser power. A first-orderSavitzky-Golay smoothing filter is further used to remove the noise inthe intensity corrected spectra. A 5th order modified polynomialconstrained to the lower bound of the smoothed spectra is thensubtracted to resolve the tissue Raman spectrum alone. The Ramanspectrum is finally normalized to the integrated area under the curvefrom 800 to 1800 cm⁻¹ to resolve the spectral line shapes and relativeintensities, reducing probe handling variations at clinical endoscopy.The GUI (FIG. 22A) illustrates the normalized spectrum at 185. In someembodiments, the spectral preprocessing module 125 utilizes additionalmethods for preprocessing including, but not limited to, multiplescatter correction (MSC), FIR filtering, weighted baseline subtraction,noise reduction, mean centering, differentiation, etc.

In step 165, the outlier analysis module 130 detects outlier spectrausing principal component (PCA) coupled with Hotelling's T² andQ-residual statistics. The GUI (FIG. 22A) illustrates the outlieranalysis at 190. The implementation of outlier detection serves as ahigh-level model-specific feedback tool in the on-line framework usingprincipal component (PCA) coupled with Hotelling's T² and Q-residualstatistics. Hotelling's T² and Q-residuals are the two independentparameters providing information of within and outside the model fit.Using these parameters as indicators of spectrum quality (i.e., probecontact mode, confounding factors, white light interference etc.),auditory feedback is integrated into the online Raman diagnostic systemfacilitating real-time spectroscopic screening and probe handling advicefor clinicians. The software system provides different sound feedbackfor different diagnostic outcomes. For instance, if the spectrum is anoutlier, a certain sound will appear. If the spectrum is diagnosticallyclassified “normal” a second distinct sound will appear. If spectrum isclassified “precancer” or “cancer” a third or fourth sound will appear.The frequency of the sound could be proportional with the “posteriorprobability”. This is very useful because it provides the endoscopistwith the real-time guidance while receiving diagnostic information.Thus, the endoscopist does not need to pay attention to the Ramanplatform monitor, but will focus on the endoscopic operation procedureswith the sound guidance. If the outlier analysis module determines thatthe acquired spectrum is an outlier, the diagnostic system 115 startsover at step 145.

If the spectra were verified for further analysis, they are fed toprobabilistic models for in vivo cancer diagnostics. In step 170 themultivariate analysis module 135 applies probabilistic models for invivo cancer diagnostics. The multivariate analysis module 135 switchesamong different pre-rendered models including partial leastsquares-discriminant analysis (PLS-DA), PCA-linear discriminant analysis(LDA), ant colony optimization (ACO)-LDA, classification and regressiontrees (CART), support vector machine (SVM), adaptive boosting (AdaBoost)etc. based on a spectral databases of large number of patients.

In step 175, the pathology module 140 implements organ specificdiagnostic models that can switch among the spectral databases ofdifferent organs for probabilistic cancer diagnostics. In addition tothe audio feedback, the GUI (FIG. 22A) provides the clinician the outputfrom the pathology module 140 at 195.

FIG. 22B provides the GUI according to a second embodiment.

The database 142 stores acquired spectra as well as the stored spectraused for diagnosis.

In some embodiments multiple spectra are taken and analyzed. For examplebetween 5-15 are taken. Each is analyzed and if more than a thresholdpercentage provides the same outcome—cancer vs normal—that is thedetermined diagnosis. For example, if 10 spectra are taken and 7 or moreprovide the same answer, that is the diagnosis. If only 5 or 6 providethe same answer, the process is repeated.

EXAMPLE

An integrated Raman spectroscopy and trimodal wide-field imaging systemused for real-time diagnostics comprises a spectrum stabilized 785 nmdiode laser (maximum output: 300 mW, B&W TEK Inc., Newark, Del., USA)electronically synchronized with a USB 6501 digital I/O (NationalInstruments, Austin, Tex., USA), a transmissive imaging spectrograph(Holospec f/1.8, Kaiser Optical Systems, Ann Arbor, Mich., USA) equippedwith a liquid nitrogen-cooled, NIR-optimized, back-illuminated and deepdepletion charge-coupled device (CCD) camera (1340 400 pixels at 20×20per pixel; Spec-10: 400BR/LN, Princeton Instruments, Trenton, N.J.,USA), and a specially designed Raman endoscopic probe for both laserlight delivery and in vivo tissue Raman signal collection. The 1.8 mmRaman endoscopic probe is composed of 32 collection fibers surroundingthe central light delivery fiber with two stages of optical filteringincorporated at the proximal and distal ends of the probe for maximizingthe collection of tissue Raman signals, while reducing the interferenceof Rayleigh scattered light, fiber fluorescence and silica Ramansignals. The Raman probe can easily pass down to the instrument channelof medical endoscopes and be directed to suspicious tissue sites underthe guidance of wide-field endoscopic imaging (WLR/AFI/NBI) modalities.The system acquires Raman spectra in the wavenumber range of 800-1800cm⁻¹ from in vivo upper GI tissue within 0.5 s using the 785 nmexcitation power of 1.5 W/cm² (spot size of 200 μm) with a spectralresolution of ˜9 cm⁻¹.

Hardware components of the Raman system (e.g., laser power control,spectrometer, CCD shutter and camera readout synchronization) wereinterfaced to the Matlab software through libraries for differentspectrometers/cameras (e.g., PVCAM library (Princeton Instruments, RoperScientific, Inc., Trenton, N.J., USA) and Omni Driver (Ocean OpticsInc., Dunedin, Fla., USA), etc.). The laser was electronicallysynchronized with the CCD shutter. The automatic adjustment of laserpower, exposure time and accumulation of spectra were realized byscaling to within 85% of the total photon counts (e.g., 55,250 of 65,000photons) based on preceding tissue Raman measurements, whereas an upperlimit of 0.5 s was set to realize clinically acceptable conditions. Theaccumulation of multiple spectra and automatic adjustment of exposuretime provides a rapid and straightforward methodology to prevent CCDsaturation and to obtain high signal to noise ratio (SNR) for endoscopicapplications. The Raman-shift axis (wavelength) was calibrated using amercury/argon calibration lamp (Ocean Optics Inc., Dunedin, Fla., USA).The spectral response correction for the wavelength-dependence of thesystem was conducted using a standard lamp (RS-10, EG&G GammaScientific, San Diego, Calif., USA). The reproducibility of the platformis continuously monitored with the laser frequency and Raman spectra ofcyclohexane and acetaminophen as wavenumber standards. All the systemperformance measures including CCD temperature, integration time, laserpower, CCD alignment are accordingly logged into a central database viaSQL server.

Real-time preprocessing of Raman signals was realized with the rapiddetection of cosmic rays using the first derivative with a 95%confidence interval (CI) over the whole spectral range set as a maximumthreshold. Data points lying outside of a threshold were interpolated to2^(nd) order. The spectra were further scaled with integration time andlaser power. A first order, 5 point Savitzky-Golay smoothing filter wasused to remove noise in the intensity corrected spectra, while a 5^(th)order modified polynomial constrained to the lower bound of the smoothedspectra was subtracted to resolve the tissue Raman spectrum alone. TheRaman spectrum was normalized to the integrated area under the curvefrom 800 to 1800 cm⁻¹, enabling a better comparison of the spectralshapes and relative Raman band intensities among different tissuepathologies. The spectra were then locally mean-centered according tothe specific database to remove common variations in the data. Followingpreprocessing, the Raman spectra were fed to a model-specific outlieranalysis.

An outlier detection scheme was incorporated into biomedicalspectroscopy as a high-level model-specific feedback tool in the on-lineframework by using PCA coupled with Hotelling's T² and Q-residualstatistics. PCA reduces the dimension of the Raman spectra bydecomposing them into linear combinations of orthogonal components(principal components (PCs)), such that the spectral variations in thedataset are maximized. The PCA model of the data matrix X is defined by:X=TP ^(T) +Ewhere T and P represent scores and loadings, and E contains theresiduals. The loadings correspond to the new rotated axis, whereasscores represent the data projection values. Accordingly, Hotelling's T²statistics is a measure of variance captured by the PCA model (sample tomodel distance) and is defined by:T _(ik) ² =t _(ik)(λ_(k) ⁻¹)t _(ik) ^(T)where t_(ik) is PC scores for i^(th) sample spectrum using component k,and λ_(k) ⁻¹λ_(k) ⁻¹ is the diagonal matrix of normalized eigenvalues ofthe covariance matrix for component k. Therefore, Hotelling's T² givesan indication of extreme values within the PCA model. On the other hand,Q-residuals is a measure of variance which is not captured by the PCAmodel (lack of model fit statistics) and is defined byQ _(ik)=Σ(x _(i) −t _(ik) P _(k) ^(T))²where x_(i) is the sample spectrum, Q_(ik) is the sum of squaredreconstruction error for i^(th) sample spectrum using component k andP_(k) is the PC loadings. For both Hotelling's T² and Q-residuals, thenormalized 99% CI was utilized as upper thresholds to interceptanomalous Raman spectra. Accordingly, the Hotelling's T² and Q-residualsare two independent parameters providing quantitative information aboutthe model fit. Using these parameters as indicators of spectra quality(i.e., probe contact mode, confounding factors, white light interferenceetc.), auditory feedback has been integrated into the online Ramandiagnostic system, facilitating real-time probe handling advice andspectroscopic screening for clinicians during clinical endoscopicprocedures.

Subsequent to verification of tissue Raman spectra quality, thosequalified Raman spectra were immediately fed to probabilistic models foron-line in vivo diagnostics and pathology prediction. The GUI caninstantly switch among different models including partial leastsquares-discriminant analysis (PLS-DA), PCA-linear discriminant analysis(LDA), ant colony optimization (ACO)-LDA, classification and regressiontrees (CART), support vector machine (SVM), adaptive boosting (AdaBoost)etc. for prospective classification at clinical endoscopic procedures.As an example, probabilistic PLS-DA was employed for gastric cancerdiagnosis. PLS-DA employs the fundamental principle of PCA but furtherrotates the components by maximizing the covariance between the spectralvariation and group affinity to obtain the diagnostically relevantvariations rather than the most prominent variations in the spectraldataset. The system supports binary classification, one-against-all andone-against-one multiclass (i.e., benign, dysplasia and cancer)probabilistic PLS-DA discriminatory analysis to predict the specifictissue pathologies.

Example 1

A total of 2748 in vivo gastric tissue spectra (2465 normal and 283cancer) were acquired from the 305 patients recruited to construct thespectral database for developing diagnostic algorithms for gastriccancer diagnostics. Tissue histopathology serves as the gold standardfor evaluation of the performance of Raman technique for in vivo tissuediagnosis and characterization.

The stomach represents one of the most challenging organs presentingwith many confounding factors (i.e., gastric juice, food debris,bleeding, exudates etc.) for spectroscopic diagnosis. The in vivo meanRaman spectra acquired from 305 gastric patients (normal (n 2465) andcancer (n=283)) for algorithms development are shown in FIG. 23. TheRaman spectra of gastric tissue show the prominent Raman peaks at 875cm⁻¹ (ν(C—C) of hydroxyproline), 936 cm⁻¹ (ν(C—C) of proteins), 1004cm⁻¹ (ν_(s)(C—C) ring breathing of phenylalanine), 1078 cm⁻¹ (ν(C—C) oflipids), 1265 cm⁻¹ (amide III ν(C—N) and δ(N—H) of proteins), 1302 and1335 cm⁻¹ (δ(CH₂) deformation of proteins and lipids), 1445 cm⁻¹ (δ(CH₂)of proteins and lipids), 1618 cm⁻¹ (ν(C═C) of porphyrins), 1652 cm⁻¹(amide I ν(C═O) of proteins) and 1745 cm⁻¹ (ν(C═O) of lipids). Gastrictissue Raman spectra contain large contribution from triglyceride (i.e.,major peaks at 1078, 1302, 1445, 1652, and 1745 cm⁻¹) that likelyreflects the interrogation of subcutaneous fat in the gastric wall. TheRaman spectra of gastric cancer reveal remarkable changes in theaforementioned Raman spectral properties (e.g., intensity, spectralshape, bandwidth and peak position), reconfirming our preceding in vivoRaman studies.

The automatic outlier detection was realized for predictive on-lineanalysis using PCA with Hotelling's T² and Q-residuals statistics (99%CI). To make the online diagnostics efficient, a two-component PCA modelwas rendered that included the largest tissue spectral variations. Theseselected significant PCs (p<0.0001) accounted for maximum variance of38.71% (PC1: 30.33%, PC2: 8.38%) of the total variability in the dataset(n=2748 Raman spectra), and the corresponding PC loadings are shown inFIG. 24.

FIG. 25 shows the score scatter plots (i.e., PC1 vs. PC2) for the normal(n=2465) and cancer tissue spectra (n=283) exemplifying the capabilityof PC scores for separating the cancer spectra from normal. The 99% CIof Hotelling's T2 and Q residuals were accordingly calculated from thetraining dataset and fixed as a threshold for prospective on-linespectral validation. We then rendered probabilistic PLS-DA models forprediction of gastric cancer. The training database was randomlyresampled multiple times (n=10) into learning (80%) and test (20%) sets.The generated PLS-DA models provided a predictive accuracy of 85.6% (95%CI: 82.9%-88.2%) (sensitivity of 80.5% (95% CI: 71.4%-89.6%) andspecificity of 86.2% (95% CI: 83.6%-88.7%)) for gastric cancerdiagnosis, retrospectively. We then further tested the outlier-detectionas well as probabilistic PLS-DA in 10 prospective gastric patients. PCscore scatter plots (i.e., PC1 vs. PC2) for the prospective normal(n=45) and cancer (n=30) tissue spectra are also shown in FIG. 25.

FIG. 26 shows the prospective scatter plot of the Hotelling's T²(38.71%) and Q-residuals (61.29%) with the 99% CI boundaries for 105spectra (45 normal, 30 cancer, 30 outlier) acquired from 10 prospectivegastric samples. The dotted line represents the 99% confidence interval(CI) verifying whether the prospective Raman spectra are within thecommon tissue variations of the principal component analysis (PCA)model. It is observed that a large number of non-contact spectra lieoutside the 99% CI and are therefore discarded in real-time withoutgoing for tissue diagnosis. The verified tissue Raman spectra largelyfall inside the 99% CI of T² and Q residuals, demonstrating that thison-line data analysis provides a rapid and highly efficient means ofreal-time validation of biomedical tissue spectra.

The prospectively acquired spectra verified by the on-line outlieranalysis are further fed to probabilistic PLS-DA for instant diseaseprediction, achieving a diagnostic accuracy of 80.0% (60/75) for gastriccancer detection (FIG. 27), as confirmed by histopathologicalexamination. The separate dotted line gives a diagnostic sensitivity of90.0% (27/30) and specificity of 73.3% (33/45) for separating cancerfrom normal gastric tissue in vivo.

The receiver operating characteristic (ROC) curves were furthergenerated to evaluate the group separations. FIG. 28 shows the mean ofthe ROC curves computed from each random splitting of the spectraldatabase for retrospective prediction as well as the ROC calculated forthe prospective dataset prediction. The integration areas under the ROCcurves generated for the retrospective and prospective datasets are 0.90and 0.92, respectively, illustrates the robustness of the PLS-DAalgorithm for gastric cancer diagnosis in vivo.

The total processing time for all the aforementioned on-line dataacquisition to tissue pathological prediction was 0.13 s. The processingtime for each step of the flow chart in FIG. 21 are given in Table 1.Free-running optical diagnosis and processing time of <0.5 s can beachieved, which is critical for realizing real-time in vivo tissuediagnostics at endoscopy.

TABLE 1 Average processing time for on-line biomedical Ramanspectroscopic framework on a personal computer 35 with a 64-bit I7quad-core 4 GB memory. Analyses Computational time (milliseconds) Cosmicray rejection 0.5 Laser response time 10 Preprocessing 40 Outlierdetection 10 Probabilistic PLS-DA prediction 70 Total computation time100 to 130

Example 2

The Raman spectroscopy system comprises a spectrum stabilized 785 nmdiode laser (maximum output: 300 mW, B&W TEK Inc., Newark, Del.), atransmissive imaging spectrograph (Holospec f/1.8, Kaiser OpticalSystems Inc., Ann Arbor, Mich.) equipped with a liquid nitrogen-cooled,back-illuminated and deep depletion CCD camera (1340×400 pixels at 20×20μm per pixel; Spec-10: 400BR/LN, Princeton Instruments, Trenton, N.J.).The system also consists of a specially designed fused-silicafiber-optic Raman endoscopic probe (1.8 mm in outer diameter and 1.30meters in length) that comprises 9×200 μm collection fibers (N.A.=0.22)surrounding the central light delivery fiber (200 μm in diameter,N.A.=0.22). A 1.0 mm sapphire ball lens (refractive index 1.76) iscoupled to the fiber tip of the Raman probe for enhancing epithelialtissue Raman measurements. The system acquires Raman spectra over therange of 800-1800 cm-1 with spectral resolution of 9 cm-1. Each Ramanspectrum in this study was measured with an integration time of 0.5 sunder the 785 nm laser excitation. The rapid Raman spectroscopytechnique was wavelength calibrated using an argon/mercury spectral lamp(AR-1 and HG-1, Ocean Optics Inc., Dunedin, Fla.). Allwavelength-calibrated spectra were corrected for the intensity responseof the system using a tungsten-halogen calibration lamp (RS-10, EG&GGamma Scientific, San Diego, Calif.).

Using the system and method described in FIGS. 20-22 was used to controlthe Raman spectroscopy system for real-time data acquisition andanalysis. The raw Raman spectra measured from in vivo tissue represent acombination of weak Raman signal, intense autofluorescence background,and noise. The raw spectra are preprocessed by a first-orderSavitzky-Golay smoothing filter (window width of 3 pixels selected tomatch the spectral resolution) to reduce the spectral noise. In thefingerprint region (800-1800 cm⁻¹), a fifth-order polynomial was foundto be optimal for fitting the autofluorescence background in thenoise-smoothed spectrum, and this polynomial is then subtracted from theraw spectrum to yield the tissue Raman spectrum alone. All theaforementioned preprocessing is completed within 100 ms and theprocessed results can be displayed on the computer screen in real-time.

The PLS regression was employed as a multivariate method to extractcharacteristic internal reference background signals from thefiber-optic Raman probe. Briefly, PLS utilizes the fundamental principleof PCA but further rotates the components LVs by maximizing thecovariance between the spectral variation and the dependent variable(e.g., laser excitation power), so that the LV loadings explain therelevant variations rather than the most prominent variations in thespectral dataset. Important spectral reference signals related to thelaser excitation power were retained in the first few LVs. In thisstudy, mean-centering was performed before modeling to reduce thecomplexity of the PLS regression model. The optimal complexity of thePLS regression model was determined through leave-one subject-out,cross-validation, and the performance of the PLS regression model wasexamined by calculating the coefficient of determination (R²), root meansquare error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV) and root mean square error of prediction (RMSEP).Note that an optimal PLS model has a high R² but with a low RMSEC,RMSECV and RMSEP. The PLS regression model developed for resolving thereference signals in this study was also implemented as an on-line laserexcitation power predictor in our real-time clinical Raman software andtested prospectively in an unbiased manner. Multivariate statisticalanalysis was conducted in the Matlab (Mathworks Inc., Natick, Mass.)programming environment.

A total of 30 normal healthy subjects (16 female and 14 males) wererecruited for in vivo tissue Raman measurements in the oral cavity.Prior to in vivo tissue Raman spectroscopy measurements, all subjectsunderwent extensive mouthwash to reduce confounding factors (e.g. fooddebris, microbial coatings etc.). In vivo tissue Raman spectra (n=783)were collected of the inner lip of 25 subjects. For the 25 subjects, invivo oral tissue Raman spectra (n=˜5) were acquired at six power levelsin the range of 5-65 mW (intervals of ˜10 mW). Before each tissue Ramanmeasurement, the laser excitation power level was measured at the distaltip of the fiber-optic probe using a power meter with a linearity of±0.5% and accuracy of ±3% (range of 0.1 to 100 mW). Other confoundingfactors (e.g., probe pressure on the tissue surface, photobleaching,tissue optical properties and bending of the fiber optic probe) were notmonitored purposely but incorporated into the PLS modeling for therobust extraction of reference signals in situ. After deployment of thedeveloped PLS model in the on-line Raman acquisition framework, theprospective and independent validation of the internal reference signalfor laser excitation power monitoring was performed on the 5 newsubjects (n=166 spectra) in real-time.

To further validate the quantitative value of the internal referencemethod developed in this work, we also conducted a tissue phantomexperiment. Tissue phantoms of various gelatin concentrations wereprepared from bovine skin, Type-B gelatin (G9391, Sigma, USA). Thegelatin was dissolved in predefined concentrations (20, 25, 30, 35, 40,45, and 50% by weight) in distilled H₂O. The dissolved gelatin washeated to 50° C. for 1 hour in a water bath with continuous stirring.Subsequently, the molten gelatin was poured into a pre-chilled mold (4°C.) and stored for 2-3 hours to produce solid gelatin phantoms.Quantitative fiber-optic Raman spectroscopic analysis of the tissuephantoms was then performed. A total of n=133 Raman spectra weremeasured from the various tissue phantoms using the fiber-optic Ramanprobe with different laser powers. The laser excitation powers werechanged in the range 10-60 mW and the measured spectra were normalizedto laser excitation powers as predicted by the internal referencemethod.

FIG. 11 shows the background spectrum of a ball-lens fiber-optic Ramanprobe used when excited by a 785 nm diode laser. The distinct sapphire(Al₂O₃) Raman peaks originating from the distal ball lens can be foundat 417 and 646 cm⁻¹ (phonon mode with A_(1g) symmetry), and 380 and 751cm⁻¹ (E_(g) phonon mode). There are two dominant Raman components fromthe fused silica fiber as well as a relatively weak fiber fluorescencebackground. The sharp “defect peaks” of fused silica denoted as D₁ andD₂ at 490 and 606 cm⁻¹, have been assigned to breathing vibrations ofoxygen atoms in four- and three-membered rings, respectively. Theshoulder (˜130 cm⁻¹) of an intense boson Raman band related to generalfeature of amorphous silica substances is also observed from thebackground spectrum of the fiber-optic Raman probe. The silica bosonband is peaking near ˜60 cm⁻¹ but only the shoulder was apparent due tothe optical filterings of our Raman probe design. These characteristicbackground Raman peaks (shorter than fingerprint region (800-1800 cm⁻¹))from the fiber-optic Raman probe itself could serve as internalreference signals for in vivo tissue Raman measurements.

To develop the PLS regression model and resolve internal referencesignals, we measured in vivo Raman spectra of 25 subjects in the oralcavity with the laser excitation power as an independent parameter. Foreach subject, in vivo tissue Raman spectra (n=˜5) were acquired withdifferent power levels in the range of 5-65 mW (intervals of ˜10 mW).FIG. 12 shows an example of the mean in vivo raw Raman spectra±1standard deviation (SD) measured from the inner lip using differentlaser excitation powers (e.g., 10, 30 and 60 mW). The weak tissue Ramansignals superimposed on the varying broad autofluorescence backgroundcan be observed. FIG. 29 shows the calibrated background-free mean Ramanspectra±1 SD. The in vivo Raman spectrum of the inner lip shows Ramanpeaks at around 853 cm⁻¹ (ν(C—C)), 1004 cm⁻¹ (ν_(s)(C—C)), 1245 cm⁻¹(amide III ν(C—N) and δ(N—H) of proteins), 1302 cm⁻¹ (CH₃CH₂ twistingand wagging), 1443 cm⁻¹ (δ(CH₂) deformation), 1655 cm⁻¹ (amide I ν(C═O)of proteins) and 1745 cm⁻¹ ν(C═O)). On the other hand, the raw in vivotissue Raman spectra (FIG. 12) also contained the prominent fused silicaand sapphire Raman peaks from the fiber-optic Raman probe, that is: 380,417, 490, 606, 646, and 751 cm⁻¹.

A PLS regression model to extract a broad range of characteristicinternal reference peaks from the oral tissue Raman spectra. TheRayleigh scattered light was excluded from PLS analysis. The measured invivo raw tissue Raman spectra were arranged in a matrix with row-wisespectra and column-wise wavenumbers. The reference laser power levelswere arranged in a column vector representing the dependent variables.After mean-centering, a PLS regression model was developed using theleave-one subject-out, cross-validation in order to establish theoptimum algorithm for rendering robust reference signals for laserexcitation power prediction. FIG. 14a shows the RMSEC and RMSECV oflaser power prediction as a function of retained LVs. The PLS regressionanalysis showed that an optimal model (RMSECV=2.5 mW) could be obtainedusing 4 LVs. FIG. 14b displays the first four LV loadings accounting forthe largest Raman spectral variance (i.e., LV1: 94.8%, LV2: 3.0%, LV3:0.9% and LV4: 0.2%) and laser excitation power variance (LV1: 80.1%,LV2: 16.8%, LV3: 0.8% LV4: 0.7%). Also shown is the calculated PLSregression vector. FIG. 30a shows the in vivo laser power monitoringresults (i.e., measured laser power vs. predicted laser power) using aleave-one subject-out, cross validation. The data can be fitted by theequation (y=0.551+0.984x) indicating a substantial linear relationship(R=0.98). The PLS model complexity of 4 LVs offered an accurate internalreference for laser excitation power monitoring with a RMSECV of 2.5 mWand R² of 0.981. The same PLS regression model was subsequentlyimplemented on-line in the Raman software for independent validation ofthe 5 new subjects (n=166 spectra) in real-time. FIG. 30b shows therelationship between the actual laser excitation power measured and thepredicted laser excitation power using the developed PLS regressionmodel. The RMSEP of 2.4 mW and a linear relationship (y=0.342+1.011x;R²=0.985) can be obtained, reconfirming the application of PLSregression as an internal reference method during in vivo tissue Ramanmeasurements.

The quantitative value of the internal-reference method developed forquantitative spectral analysis of tissue phantoms. Seven tissue phantomscomposed of gelatin with different concentrations (i.e., 20, 25, 30, 35,40, 45, and 50% by weight) were constructed and tested. Raman spectra(n=133 spectra) from gelatin phantoms were measured and normalized tothe laser powers predicted in real-time. FIG. 31 shows the Raman spectrameasured from gelatin tissue phantoms with different concentrations at60 mW excitation laser power. As expected, these Raman spectra show alinear relationship (R=0.992) between the Raman peak intensities andgelatin concentrations. FIG. 32 shows the correlationship between theactual gelatin concentrations and the predicted concentrations withvarying excitation laser powers (varying from 10 to 60 mW). It isevident that by correcting the laser power variation through real-timelaser excitation power monitoring in situ, accurate quantitativeanalysis of gelatin tissue phantoms can be realized (RMSEP=1.9% andR²=0.985). The above results indicate that the developed real-time powermonitoring method based on multivariate internal reference signals canachieve robust quantitative compositional analysis in fiber-optic tissueRaman spectroscopy.

Example 3—In Vivo Real-Time Transnasal Image-Guided Raman Endoscopy:Defining Spectral Properties in the Nasopharynx and Larynx

This study demonstrates the feasibility of Raman spectroscopy intransnasal endoscopic applications, providing the foundation forlarge-scale clinical studies in the head and neck. The image-guidedRaman endoscopy platform integrated with a miniaturized fiber Ramanprobe developed provides a rapid and minimally invasive assessment ofendogenous tissue constituents of the head and neck at the molecularlevel during clinical endoscopic examination. This greatly facilitatesclinicians to obtain detailed biomolecular fingerprints of tissue in thehead and neck, reflecting the genuine compositional and morphologicalsignatures without introducing the artifacts caused by vascularpuncturing or tissue dehydration, morphological and anatomical effects,etc.

The Raman spectroscopy system consists of a spectrum stabilized 785 nmdiode laser (maximum output: 300 mW, B&W TEK Inc., Newark, Del.), atransmissive imaging spectrograph (Holospec f/1.8, Kaiser OpticalSystems) equipped with a cryogenic cooled (−120° C.), NIR-optimized,back-illuminated and deep depletion charge-coupled device (CCD) camera(1340×400 pixels at 20×20 μm per pixel; Spec-10: 400BR/LN, PrincetonInstruments). The novel spectrometer fiber input coupling consists ofparabolic aligned array of 58 fibers (100 μm) to correct thespectrometer image aberration for improving both the spectral resolutionand signal-to-noise ratio of Raman signals. A 1.8 mm fiber-optic Ramanprobe for transnasal endoscopic applications maximizing both the tissueexcitation and in vivo tissue Raman collections was utilized. The Ramanfiber probe fits into the instrument channel of flexible transnasalendoscopes and can be safely directed to different locations in thenasopharynx and larynx under the wide field imaging (i.e., white-lightreflectance (WLR) and narrowband imaging (NBI)) guidance. The clinicalRaman endoscopy platform has been integrated with our recently developedon-line data processing software to facilitate probe handling-advise andsound feedback to clinicians in real-time (processing time<0.1 s).Briefly, the on-line Raman endoscopy framework synchronizes spectralacquisition (i.e., laser exposure, integration time, CCD shutter andreadout etc.) and automatically extracts the Raman signals from the rawtissue spectra (comprising strong autofluorescence background and weakRaman signals) using the established preprocessing methods includingsmoothing, fifth-order polynomial baseline subtraction etc. The in vivoRaman spectra and the outcome of multivariate algorithms (e.g.,principal component analysis) can be displayed in real-time in acomprehensible graphical user interface (GUI) during clinical transnasalRaman endoscopy.

A total of 23 normal healthy male subjects of different races(twenty-two Asian and one Caucasian) were recruited for in vivo tissueRaman measurements at transnasal endoscopy. In these subjects recruited,no suspicious lesions were identified under the WLR and NB imagingexamination. A total of three primary measurement sites of assumednormal (or benign) tissues were predefined for in vivo Ramanacquisitions, including the true laryngeal vocal cords (LVC), theposterior nasopharynx (PN), and also the pharyngeal recess (i.e., fossaof Rosenmüller (FOR)) where NPC typically initiates. The fiber-opticRaman probe can be placed in gentle contact with internal tissuesinterrogating with the endogenous biomolecular compositions of tissue inreal-time. The accurate positioning against the biopsied tissue siteswas verified on the WLR/NBI monitor by the endoscopists in-charge. Theprobe allowed Raman spectra to be collected from an area (200 μm indiameter) with probing volume of approximately 1 mm³ and penetrationdepth of ˜800 μm. Each spectrum was acquired within 0.5 s using the 785nm laser light with the power of ˜50 mW on the tissue surface.

The Raman spectra were displayed on-line and were stored forpost-procedural inspection. This rapid Raman endoscopic technology isnon-destructive, and can now routinely be used under endoscopictransnasal examinations for clinical evaluation. To assess theintra-tissue site variance, several Raman spectra (˜18) were alsoacquired from each tissue site. As a result, a total of 874 in vivoRaman spectra from 47 sites were measured at transnasal endoscopy andused for spectral analysis [PN (n=521), FOR (n=157) and LVC (n=196)]from the 23 subjects.

Prior to data-analysis, the raw Raman spectra were firstly smoothedusing a linear Savitzky Golay filter, and tissue autofluorescencebackground was then subtracted from the smoothed spectra using a 5^(th)order polynomial fit. The background-subtracted Raman spectra werenormalized to the integrated areas under the curves to minimize theeffect of Raman probe handling variations on clinical Raman measurementswith respect to different subjects and tissue sites. All processed Ramanspectra were assembled into a matrix, and the mean centering of theentire Raman dataset was then performed. To reduce the dimension of thespectral data, principal component analysis (PCA) was employed toextract a set of orthogonal principal components (PCs) that account forthe maximum variance in the Raman spectral dataset for tissuecharacterization. Accordingly, loadings on the PCs represent orthogonalbasis spectra of the most prominent spectral variation in the datasetaccounting for progressively decreasing variance, whereas the scores onthe PCs represent the projection value of the tissue Raman spectra onthe corresponding loading. Thus, PCA can efficiently be used to resolvespectral variations while reducing the dimension of the dataset to aminimum. The number of retained PCs was chosen based on the analysis ofvariance (ANOVA) and Student's t-test at 0.05 level. We employedpost-hoc Fisher's least squares differences (LSD) test to assessdifferences in means. Multivariate statistical analysis was performedusing the PLS toolbox (Eigenvector Research, Wenatchee, Wash.) in theMatlab (Mathworks Inc., Natick, Mass.) programming environment.

High quality in vivo Raman spectra can routinely be acquired in thenasopharynx and larynx in real-time during transnasal image-guided(i.e., WLR and NBI) endoscopic inspections. FIG. 1 shows an example ofin vivo raw Raman spectrum (weak Raman signal superimposed on largetissue autofluorescence background) acquired from the posteriornasopharynx with an acquisition time of 0.1 s at endoscopy. Thebackground-subtracted tissue Raman spectrum with a signal-to-noise ratio(SNR) of >10 (Inset of FIG. 33) can be obtained and displayed onlineduring clinical endoscopic measurements. FIG. 34 depicts theinter-subject in vivo mean Raman spectra±1 standard deviations (SD) ofnormal nasopharyngeal [PN (n=521) and FOR (n=157)] and laryngeal tissues[LVC (n=196)] when the Raman probe is gently contacted with the tissueunder WLR/NB imaging guidance. Comparisons with the nasopharyngeal andlaryngeal tissue Raman spectra acquired (FIG. 34), demonstrates thatthose biochemical in the body fluids do not contribute significantly tothe in vivo tissue Raman spectra at transnasal endoscopy. Also shown isWLR images obtained from the corresponding anatomical locations.Prominent Raman bands associated with proteins and lipids are identifiedas tabulated in Table 2 with tentative biomolecular assignments.

TABLE 2 Tentative assignments of molecule vibrations and biochemicalsinvolved in Raman scattering of nasopharyngeal and laryngeal tissue(wherein ν, stretching mode; ν_(s), symmetric stretching mode; δ,bending mode). Raman peaks (cm⁻¹) Vibrations Biochemicals 853 ν(C—C)proteins 940 ν(C—C) proteins 1004 ν_(s)(C—C) breathing proteins 1078ν(C—C) lipids 1265 Amide III ν(C—N) δ(N—H) proteins 1302 CH₂ twistingand wagging lipids/proteins 1450 δ(CH₂) lipids/proteins 1660 Amide Iν(C═O) proteins

FIG. 35 shows the intra-subject mean spectra±1 SD of a randomly chosensubject. The in vivo tissue Raman spectra were found to be reproduciblewith diminutive inter- and intra-subject variances (<10%) in thenasopharynx and larynx. Further Raman endoscopic testings indicate thatthe variability between different tissue sites within the posteriornasopharynx is subtle (<5%) (data not shown). We also calculateddifference spectra±1 SD between different tissue types (i.e., PN-LVC,LV-FOR and PN-FOR) as shown in FIG. 36, resolving the distinctivecompositional and morphological profiles of different anatomical tissuesites at the biomolecular level. ANOVA revealed twelve prominent andbroad Raman spectral sub-regions that showed significant variability[p<0.0001] between the three anatomical tissue sites centered at: 812,875, 948, 986, 1026, 1112, 1254, 1340, 1450, 1558, 1655 and 1745 cm⁻¹,reconfirming the importance of characterizing the Raman spectralproperties of nasopharynx and larynx toward accurate in vivo tissuediagnostics.

In vitro Raman spectra of blood, saliva and nasal mucus obtained fromhealthy volunteers were measured as shown in FIG. 37. The most prominentRaman bands in saliva and nasal mucus are at 1638 cm⁻¹ (v₂ bending modeof water), whereas blood exhibits porphyrin Raman bands nearby 1560 and1620 cm⁻¹ ³¹. To further assess the spectral differences among differenttissues in the head and neck, a five-component PCA model based on ANOVAand student's t-test (p<0.05) accounting for 57.41% of the totalvariance (PC1: 22.86%; PC2: 16.16%; PC3: 8.13%; PC4 6.22% PC5: 4.05%)was developed to resolve the significant peak variations of differentanatomical locations. FIG. 38 shows the PC loadings revealing theresolve Raman bands associated with proteins (i.e., 853, 940, 1004,1265, 1450 and 1660 cm⁻¹) and lipids (i.e., 1078, 1302 1440, 1655 and1745 cm⁻¹). FIG. 39 (A to E) displays box charts of PCA scores for thedifferent tissue types (i.e., PN, FOR and LVC). The line within eachnotch box represents the median, and the lower and upper boundaries ofthe box indicate first (25.0% percentile) and third (75.0% percentile)quartiles, respectively. Error bars (whiskers) represent the 1.5-foldinterquartile range. The p-values are also represented among differenttissue types. Dichotomous PCA algorithms integrated with lineardiscriminant analysis (LDA) provided the sensitivities of 77.0%(401/521), 67.3% (132/192) and specificities of 89.2% (140/157) and76.0% (396/521) for differentiation between PN vs. FOR, and LVC vs. PN,respectively using leave-one subject-out, cross validation. Overall,these results demonstrate that Raman spectra of nasopharynx and larynxin the head and neck can be measured in vivo at transnasal endoscopy,and the diagnostic algorithms development should be tissue site specificto ensure minimum algorithm complexity.

Example 4—Fiber-Optic Confocal Raman Spectroscopy for Real-Time In VivoDiagnosis of Dysplasia in Barrett's Esophagus

Fiber-optic confocal Raman diagnostics can be achieved in real-time(<0.5 second) and uncovers the progressive biomolecular and functionalchanges of epithelial cells and tissues in Barrett's carcinogenesis insitu. Histopathology characterized 152 of the prospectively measuredtissue sites as columnar lined epithelium (n=597 spectra), 48 asintestinal metaplasia (n=123 spectra), 9 high-grade dysplasia (n=77spectra). Using receiver operating characteristics (ROC) analysis,identification of high-grade dysplasia could be successfully achievedyielding a sensitivity of 87.0%, and a specificity of 84.7% on spectrumbasis. The area under the ROC curve was found to be 0.90. This newbiomolecular specific endoscopic modality with real-time capabilityoffers the gastroenterologist a reliable tool to objectively targethigh-risk tissue areas in Barrett's patients during ongoing endoscopy.

The confocal Raman spectroscopic system comprises of a near-infrared(NIR) diode laser (λ_(ex)=785 nm), a high-throughput transmissiveimaging spectrograph equipped with a liquid nitrogen-cooled,NIR-optimized charge-coupled device (CCD) camera and a speciallydesigned 1.8-mm fiber-optic confocal Raman probe. The system acquiresRaman spectra in the range 800-1800 cm⁻¹ with a spectral resolution of˜9 cm⁻¹. The developed fiber-optic confocal Raman endoscopic probe isused for both laser light delivery and in vivo tissue Raman signalcollection.

The 1.8 mm (in outer diameter) confocal Raman endoscopic probe comprises9×200 μm filter-coated collection fibers (NA=0.22) surrounding thecentral light delivery fiber (200 μm in diameter, NA=0.22). A miniature1.0 mm sapphire ball lens (NA=1.78) is coupled to the fiber tip of theconfocal probe to tightly focus the excitation light onto tissue,enabling the effective Raman spectrum collection from the epitheliallining (<200 μm). The fiber-optic confocal Raman probe can be insertedinto the instrument channel of conventional endoscopes and placed ingentle contact with the epithelium for in vivo tissue characterizationand diagnosis. The depth-selectivity of this confocal Raman probe offerscompelling experimental advantages, including (i) fiber-optic confocalRaman spectroscopy selectively targets the epithelial lining associatedwith early onset of Barrett's carcinogenesis, which is superior toconventional volume-type fiber-optic Raman probes that interrogate alarger tissue volume; (ii) the shallower tissue interrogation ability ofconfocal Raman technique provides a higher tissue Raman toautofluorescence background ratio due to a much reduced tissueautofluorescence contribution from deeper tissue layers (e.g., stroma),and (iii) combining this novel fiber-optic confocal Raman spectroscopyplatform with well-documented multivariate analysis enables epithelialmolecular information to be extracted and analyzed in real-time in vivo.The entire confocal Raman endoscopic system is controlled in anintuitive software framework that permits rapid survey in endoscopicscreening settings with auditory probabilistic feedback to theendoscopist, pushing the frontier of Raman spectroscopy into routineclinical diagnostics.

A total of 450 patients have been enrolled in the Raman endoscopicexaminations for surveillance or screening of various indications,including dyspepsia and upper GI neoplasia. During a typical examinationof suspicious lesions, each tissue Raman measurement can be acquiredwithin 0.5 second, which permits rapid survey of large tissue areas. Thein vivo Raman spectral data acquired from 373 patients with differenthistological subtypes in the upper GI have been used to construct acomprehensive Raman library (>12,000 Raman spectra). For the patientsrecruited for screening and surveillance of BE, Raman spectra arecategorized into following three histopathologically risk classes: (i)“Normal”—columnar lined epithelium (CLE), (ii) “Low-risk” BE-defined asthe presence of goblet cells, (iii) “High-risk”-low-grade dysplasia(LGD) and high-grade dysplasia (HGD). For example, FIG. 40A shows themean in vivo confocal Raman spectra measured from patients in ourdatabase presenting with different tissue types (i.e., squamous linedepithelium (n=165), CLE (n=907), intestinal metaplasia (IM) (n=318) andHGD (n=77)) as confirmed by histopathological characterization. EachRaman spectrum was acquired within 0.5 sec. The spectra have beennormalized to the Raman peak at 1445 cm⁻¹ for comparison purpose.Prominent tissue Raman peaks can be observed at around: 936 cm⁻¹ (ν(C—C)proteins), 1004 cm⁻¹ (ν_(s)(C—C) ring breathing of phenylalanine), 1078cm⁻¹ (ν(C—C) of lipids), 1265 cm⁻¹ (amide III ν(C—N) and δ(N—H) ofproteins), 1302 cm⁻¹ (CH₂ twisting and wagging of proteins), 1445 cm⁻¹(δ(CH₂) deformation of proteins and lipids), 1618 cm⁻¹ (ν(C═C) ofporphyrins), 1655 cm⁻¹ (amide I ν(C═O) of proteins) and 1745 cm⁻¹(ν(C═O) of lipids). Remarkable Raman spectral differences (e.g., peakintensity, shifting and band broadening) can be discerned amongdifferent tissue types. These rich spectral signatures portray thebiomolecular and functional changes occurring in the epitheliumaccompanying Barrett's carcinogenesis. While histology identifiespresence of goblet cells and progressive architectural and cytologicalatypia (FIG. 40(B, C, D, E), fiber-optic confocal Raman spectroscopyreveals that the epithelium undergoes major functional and biomolecularchanges throughout Barrett's carcinogenesis sequence. It is intriguingthat the Raman biomolecular signature of BE resembles that of dysplasiato a high degree, confirming that transformation to intestinalmetaplastic phenotype is a key event in Barrett's carcinogenesis. Thesehighly specific epithelial molecular signatures possibly reflect amultitude of endogenous optical biomarkers (i.e., oncoproteins, DNA,mucin expression, mitoses, etc.).⁸ Therefore, correlation of theepithelial Raman spectral signatures with histopathology orhistochemistry can deepen the understanding of Barrett's onset andprogression in situ at the biomolecular level. Currently, no othercompeting optical spectroscopic techniques (e.g., fluorescence, elasticscattering spectroscopy) can provide such exhaustive molecularcharacterization in vivo at endoscopy.

Histopathology characterized 152 of the prospectively (i.e.,independently) measured tissue sites as CLE (n=597 spectra), 48 as IM(n=123 spectra) and 9 as HGD (n=77 spectra). FIG. 41A shows atwo-dimensional ternary scatter plot of the prospective measured riskscores in 77 patients belonging to confocal Raman spectra of normal,low-risk and high-risk lesions. The corresponding dichotomous receiveroperating characteristic (ROC) curves (FIG. 41B) are also generated fromFIG. 3A with the area under curve (AUC) being 0.88, 0.84 and 0.90,respectively, for discriminations among normal, low-risk and high-risklesions. Not only did the confocal Raman technique differentiate thelow-risk lesions with BE (FIG. 41A), it was also able to objectivelylocalize the specific tissue areas containing dysplastic epithelium. Theabove ROC analysis illustrate that the targeted detection of high-risktissues can be successfully achieved in real-time, yielding a diagnosticsensitivity of 87.0% (67/77), and a specificity of 84.7% (610/720) onspectrum basis.

In the above description, an embodiment is an, example or implementationof the disclosed system and methods. The various appearances of “oneembodiment”, “an embodiment” or “some embodiments” do not necessarilyall refer to the same embodiments.

Although various features of the disclosed system and methods may bedescribed in the context of a single embodiment, the features may alsobe provided separately or in any suitable combination. Conversely,although the disclosed system and methods may be described herein in thecontext of separate embodiments for clarity, the disclosed system andmethods may also be implemented in a single embodiment.

Furthermore, it is to be understood that the disclosed system andmethods can be carried out or practiced in various ways and can beimplemented in embodiments other than the ones outlined in thedescription above.

Meanings of technical and scientific terms used herein are to becommonly understood as by one of ordinary skill in the art belong,unless otherwise defined.

Certain aspects of the present disclosure include process steps andinstructions described herein in the form of a method. It should benoted that the process steps and instructions of the present disclosurecould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a tangible non-transitory computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The methods and operations presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may also be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will be apparent to those of skill in theart, along with equivalent variations. In addition, the presentdisclosure is not described with reference to any particular programminglanguage. It is appreciated that a variety of programming languages maybe used to implement the teachings of the present disclosure asdescribed herein, and any references to specific languages are providedfor disclosure of enablement and best mode of the present disclosure.

The present disclosure is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet, publicnetworks, private networks, or other networks enabling communicationbetween computing systems. Finally, it should be noted that the languageused in the specification has been principally selected for readabilityand instructional purposes, and may not have been selected to delineateor circumscribe the inventive subject matter. Accordingly, thedisclosure of the present disclosure is intended to be illustrative, butnot limiting, of the scope of the disclosure, which is set forth in thefollowing claims.

The invention claimed is:
 1. A method of calibrating a fiber optic Ramanspectroscope system, the system comprising a processor configured toexecute program instructions, a storage device, a laser source, aprimary spectrometer system and a plurality of fiber optic probes,including at least a first optic probe and a second optic probe, each ofthe plurality of fiber optic probes couplable to the primaryspectrometer system and each of the plurality of fiber optic probesconfigured to transmit light from the laser source to a target andreturn scattered light to the primary spectrometer system the methodcomprising: storing a known spectrum for a standard target in thestorage device; transmitting light from the laser source to the standardtarget having the known spectrum; and for each of the plurality of fiberoptic probes: recording a calibration spectrum of the scattered lightreturned to the primary spectrometer system through each of theplurality of fiber optic probes from the standard target; comparing theknown spectrum and the calibration spectrum and generating with the useof the processor at least a first transfer function for the first opticprobe and a second transfer function for the second optic probe;calculating a calibration function based on at least the first transferfunction and second transfer function; storing the first transferfunction, the second transfer function and the calibration function inthe storage device to thereby associate at least the first fiber opticprobe with the first transfer function and the secondary fiber opticprobe with the second transfer function.
 2. The method according toclaim 1 further comprising: subsequently illuminating a test subjectusing a selected fiber optic probe within the plurality of fiber opticprobes; recording a spectrum while illuminating the test subject; andcorrecting the spectrum in accordance with the stored transfer functionassociated with the selected fiber optic probe.
 3. The method accordingto claim 1 wherein the spectrometer has an associated spectrometertransfer function and the probe has an associated probe transferfunction, and the transfer function is a function of the spectrometertransfer function and the probe transfer function.
 4. The methodaccording to claim 1 comprising associating the calibration functionwith the secondary fiber optic probe.
 5. The method according to claim 1comprising, on a secondary spectrometer system, using the primary fiberoptic probe and generating a secondary system transfer function andstoring the secondary system transfer function.
 6. The method accordingto claim 5 comprising using the secondary fiber optic probe with thesecondary spectrometer system and modifying the stored secondary systemtransfer function in accordance with the calibration function.
 7. Themethod according to any one of claims 1 comprising an initial step ofperforming a wavelength-axis calibration of the secondary spectrometersystem in accordance with the primary spectrometer system.
 8. The methodaccording to claim 1, further comprising: transmitting light from thelaser source to a plurality of targets; for each target, measuring thetransmitted power of the light from the laser source and the capturedspectrum of the scattered light at the spectroscope; performing amultivariate analysis of the captured spectra with the measuredtransmitted power as a dependent variable; and storing a resulting modelof laser power as a function of spectral characteristics of the capturedspectra.
 9. The method according to claim 8, further comprising:transmitting laser light to a test target; supplying a captured spectrumto the model; and calculating an estimate of the transmitted power. 10.A method of operating a fiber optic Raman spectroscope system within aplurality of fiber optic Raman spectroscope systems, the plurality offiber optic Raman spectroscope systems including each of a master Ramanspectroscope system having a master spectrometer and a laser source, andat least one secondary Raman spectroscope system, each secondary Ramanspectroscope system having a corresponding secondary spectroscopedistinct from the master spectroscope and a laser source, the masterRaman spectroscope system couplable to a primary fiber optic probeand/or a plurality of secondary fiber optic probes, each secondary Ramanspectroscope system couplable to the primary fiber optic probe and/orthe plurality of secondary fiber optic probes, the method comprising:(A) storing a known spectrum for a standard source and providing thestandard source having the known spectrum; and (B) for each secondaryRaman spectroscope system, performing a wavelength calibration of thesecondary spectrometer thereof to the master spectrometer of the masterRaman spectroscope system; and (C) performing a first calibrationprocess for each secondary Raman spectroscope system, the firstcalibration process comprising: (i) determining a plurality of transferfunctions, each of the plurality of transfer functions corresponding toa selected secondary fiber optic probe coupled to the secondary Ramanspectroscope system, each of the plurality of transfer functionsestablishing a mathematical relationship between the known spectrum anda measured calibration spectrum obtained while the selected secondaryfiber optic probe is coupled to the secondary Raman spectroscope system;and (ii) associating each selected secondary fiber optic probe with thetransfer function determined therefor; or (D) performing a secondcalibration process comprising: (i) for each secondary Ramanspectroscope system: (a) determining a system transfer functioncorresponding to the secondary Raman spectroscope system while theprimary fiber optic probe is coupled thereto during measurement therebyof a calibration spectrum of scattered light received from the standardsource; and (b) associating the secondary Raman spectroscopy system withthe system transfer function determined therefor; and (ii) for eachsecondary fiber optic probe: (a) determining a calibration function forthe secondary fiber optic probe, the calibration function correspondingto the secondary fiber optic probe coupled to the master Ramanspectroscope system during measurement of a spectrum of scattered lightreceived from the standard source; and (b) associating the secondaryfiber optic probe with the calibration function determined therefor. 11.The method according to claim 10 wherein the plurality of secondaryfiber optic probes comprises a plurality of reserve probes for aselected secondary Raman spectroscope system, and wherein the firstcalibration process is performed for the selected secondary Ramanspectroscope system to determine a transfer function corresponding toeach of the reserve probes.
 12. The method according to claim 10comprising performing the second calibration process to match anyplurality of secondary fiber optic probes to any number of secondaryRaman spectroscope systems such that spectra captured using differentsecondary Raman spectroscope systems and different secondary fiber opticprobes are consistent and comparable.
 13. A Raman spectroscope systemcomprising: a laser source; a primary spectrometer system; a pluralityof fiber optic probes, including at least a first fiber optic probe anda secondary fiber optic probe, each of the plurality of fiber opticprobes couplable to the primary spectrometer system and, when coupled tothe primary system, configured to transmit light from the laser sourceto a target and return scattered light to the primary spectrometersystem; a storage device; a first transfer function for the primaryfiber optic probe and a second transfer function for the secondary opticprobe stored in the storage device; a calibration function based on thefirst transfer function and the second transfer function; and a computerhaving a processor configured to execute program instructions such thatthe system is operable to: transmit light from the laser source to atarget having a known spectrum using a selected one of the plurality offiber optic probes; record a spectrum of the scattered light from thetarget; and modify the recorded spectrum in accordance with the storedtransfer function corresponding to the selected fiber optic probe. 14.The system according to claim 13 wherein the stored transfer functioncorresponds to the spectrometer and the selected fiber optic probe. 15.The system according to claim 13 wherein the stored transfer functioncorresponds to the spectrometer and a primary fiber optic probe distinctfrom the selected fiber optic probe, and the computer is furtherconfigured to execute program instructions such that the system isoperable to modify the stored transfer function in accordance with astored calibration function associated with the selected fiber opticprobe.